Image Feature Matching Via Parallax Mapping for Close Range Photogrammetric Application

Abstract

Image feature matching is an important step in close range photogrammetric applications, and implementing a fast, accurate and robust feature-based image matching technique is a challenging task. To solve the problems of traditional image feature matching algorithms, such as their low accuracy and long processing time, we present a parallax mapping-based matching (PMM) method that is able to improve the computation efficiency, accuracy and robustness of feature-based image matching for close range photogrammetric applications. First, the disparity of the initial corresponding points is calculated, and the mismatches are initially removed via local disparity clustering. Then, the local coordinates of the matching points are constrained by an image division grid. To ensure the correctness of the matching points, the fast polynomial transform is used for as a quadratic constraint. The method can extract high accuracy matching points from the original coarse matching points with low accuracy, and also preserve the true matching points to the greatest extent. Using a variety of experimental datasets and current mainstream feature extraction algorithms, we designed and compared commonly used feature-based image matching algorithms. The experimental results show that the proposed method is simple but effective, can meet the real-time calculation requirements, and outperforms the current state-of-the-art methods.